Detecting bleeding events using EHRs for prediction in Afib

摘要 房颤（AF）影响着数百万美国人，其中大多数是老年人。房颤 有助于中风，并权衡中风风险与口腔出血风险 抗凝剂（AC）是AF管理的核心。AC决策已成为 近年来随着几种靶向特异性口服AC的引入， 剂.向老年房颤患者提供关于AC的建议仍然具有挑战性，因为他们 经常处于中风和AC并发症的高风险中，特别是出血，以及 关于弱势群体中AC的真实结果的数据有限。我们 项目，使用心房预测电子记录检测出血事件 房颤（DEEP AF）将利用新型生物医学自然语言处理 (NLP)整合来自结构化和 非结构化EHR记录以及统计和深度学习方法来预测 接受AC治疗的AF患者发生出血。DEEP AF将解决目前的知识差距 从纵向电子健康记录中准确识别出血事件， 提高出血预测的准确性，重点关注可改变的临床相关风险 因素，从而促进未来的大规模比较有效性研究 评估AC试剂，并帮助临床医生开发更好的方法来衡量AC 风险和收益。

Abstract Atrial fibrillation (AF) affects millions of mostly older Americans. Atrial fibrillation contributes to stroke and weighing stroke risk against risk of bleeding from oral anticoagulants (AC) is central to AF management. AC decision-making has become more complex in recent years with the introduction of several target specific oral AC agents. It remains challenging to advise older AF patients about AC since they are frequently at high risk for stroke and complications from AC, particularly bleeding, and data are limited on real-world outcomes of AC among vulnerable populations. Our project, Detecting bleeding Events using Electronic records for Prediction in Atrial Fibrillation (DEEP AF), will leverage novel biomedical natural language processing (NLP) approaches to integrate bleeding-related information from both the structured and unstructured EHR records and statistical and deep learning approaches to predict risk of bleeding on AF patients on AC therapy. DEEP AF will address present knowledge gaps to accurately identify bleeding events from longitudinal electronic health records and to improve bleeding prediction accuracy and focus on modifiable, clinically relevant risk factors, thereby facilitating future large-scale comparative effectiveness studies evaluating AC agents, and helping clinicians develop better approaches to weighing AC risks and benefits.